Dynamic Equilibrium Model of Expressway Toll Collector Based on Traffic Flow Prediction

Abstract

Based on the excellent characteristics of neural network such as favorable adaptability to incomplete information and of genetic algorithm with relatively strong and comprehensive search ability, these two objectives are combined to predict the short-term traffic flow of expressway. In this paper, the methods of elite's choices, fitness assignment by proportion and by sort are combined, as well as self-adaptive crossing and mutation probability are used to improve genetic algorithm. To enhance the comprehensive optimal search speed and develop the prediction model for expressway short-term traffic flow, self-adaptive learning rates are introduced to improve BP algorithm and new combing methods are presented to get new species. Meanwhile, queuing theory is adopted to simulate toll process of expressways, and then queuing model of expressways' toll stations is formulated. By combining short-term traffic flow prediction model and toll station queuing model, the demands of toll collectors are predicted according to the allocation of lanes and toll collectors, which realizes the dynamic optimal equilibrium of toll collectors. The validity of the model is finally testified by field tests.